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Decision tree, as an important classification algorithm in data mining, has been successfully applied in many fields. In this paper, based on the analysis of the essential characteristics of decision tree algorithm, we give a leaf criterion for multi-decision values of decision attribute, and establish a mathematical model for the selection for expanded attributes; also we give a concrete model based...
Decision tree, as a simple classification algorithm, is an effective tool for mining knowledge rules, and it has been successfully applied in many fields. Based on the selection problems of the expanded attributes, we put forward quasi-linear leaf criterion and data utilization criterion which can recognize the extension ability of attributes, and give a selection model of expanded attributes based...
Decision tree, as a simple classification algorithm, is an effective tool for mining knowledge rules, and it has been successfully applied in many fields. Based on the analysis of the essential characteristics of decision tree algorithm, for the selection of the expanded attributes, we put forward leaf criterion; data utilization criterion and comprehensive effect criterion which can recognize the...
Classification algorithm is a kind of important technology in data mining, and the most commonly used is decision tree learning. In the process of constructing a decision tree, the selecting criteria of splitting attributes will directly affect the classification results. And the attribute selection of the traditional decision tree algorithm is based on information theory. In this paper, by combining...
In this paper, for the refinement of the database in data mining, by synthetically analyzing the characteristics of the current attribute reduction methods and decision tree algorithm, we put forward formalized description model of rule knowledge, and establish a kind of attribute reduction method (BD-RED) of decision tree by using similarity between rules families. Further, we discuss the construction...
The attribute reduction of information system can improve the accuracy of knowledge discovery, machine learning, etc. and it also can improve the efficiency. This paper proposes an attribute testing reduction algorithm, the algorithm can make the information system retain as few as attributes under the condition that maintains the original style, it can not only save much time for the later system...
In the inductive learning, if example bank has noise, it is hard to obtain decision trees with high precision, that is, it is hard to obtain knowledge with high credibility. So, this paper puts forward C-ID3 algorithm, by which we compute confidence level of example bank firstly, and further get a kind of decision tree on the basis of traditional ID3 and the credibility obtained. All the theory analysis...
Decision tree algorithm is not only the important part of machine learning, but also the most widely used data mining tool. At present, there are many algorithms of generating decision tree, but when the database which we rely on exists noise, high quality knowledge is hard to obtain by ID3 algorithm. In this paper, we propose the data mining method based on second learning in case of ID3 algorithm,...
When noise exists in case base, high quality knowledge is hard to obtain by ID3 algorithm. For the weakness, by introducing the concept of second learning, the noisy data can be removed, which not only develop the decision tree, but also it can make good structure tree generate. So that we can abstract good rules information, and make the desirable tree more accurate. Especially, the more the data...
Induce learning is one of the most important research areas on application of artificial intelligence. The classification algorithm is the core of the machine learning. The article advanced a kind of decision classification algorithm that is in view of attribute value statistical regularity, on the characteristic and insufficient of ID3, C4.5 algorithm. (In short of BS-CA) This algorithm takes the...
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